






'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
  import torch

  from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'	)
         a_						     =			torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]	)
         # The dog is cute and lives in the garden house

         a_						     =			torch.Size((1, 12, 768)	)  # batch_size, sequence_length, embedding_vector_dim
         a_						     =			torch.tensor(
             [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]	)
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
                a_						     =			model(UpperCamelCase__	)['last_hidden_state'].detach()
         self.assertEqual(output.shape   ,	UpperCamelCase__	)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]   ,	UpperCamelCase__   ,	atol=1e-3	)	)







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'	)
         a_						     =			torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]	)
         # The dog is cute and lives in the garden house

         a_						     =			torch.Size((1, 12, 1_024)	)  # batch_size, sequence_length, embedding_vector_dim
         a_						     =			torch.tensor(
             [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]	)
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
                a_						     =			model(UpperCamelCase__	)['last_hidden_state'].detach()
         self.assertEqual(output.shape   ,	UpperCamelCase__	)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]   ,	UpperCamelCase__   ,	atol=1e-3	)	)












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase        =							{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							["GLPNFeatureExtractor"]
  __lowerCAmelCase        =							["GLPNImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							[
      "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GLPNForDepthEstimation",
      "GLPNLayer",
      "GLPNModel",
      "GLPNPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




